TMDB MCP Server Integration

What does “TMDB” MCP Server do?

The TMDB MCP Server connects AI assistants with The Movie Database (TMDB) API, providing seamless access to extensive movie information, search capabilities, and movie recommendations. Acting as a bridge between AI clients and TMDB, it enables tasks such as searching for movies by title or keyword, retrieving detailed information about specific films, and obtaining trending movies or personalized recommendations. This integration streamlines workflows for developers building entertainment-related applications, chatbots, or assistant features, allowing AI systems to query movie databases, manage contextual information, and interact with TMDB resources programmatically. The TMDB MCP Server enhances development processes by standardizing and simplifying how AI agents access and present movie data from TMDB.

List of Prompts

No prompt templates are mentioned in the available documentation.

Ready to grow your business?

Start your free trial today and see results within days.

Request demo Log in

List of Resources

• Movies (tmdb:///movie/<movie_id>)
  Provides comprehensive movie details, including:
  • Title and release date
  • Rating and overview
  • Genres
  • Poster URL
  • Cast information (top 5 actors)
  • Director
  • Selected reviews
    All data is returned in JSON format.

List of Tools

• search_movies
  Search for movies by title or keywords. Returns a list of movies with titles, release years, IDs, ratings, and overviews.

• get_recommendations
  Get movie recommendations based on a specific TMDB movie ID. Returns the top 5 recommended movies with details.

• get_trending
  Retrieve trending movies for a specified time window (“day” or “week”). Returns the top 10 trending movies with details.

Join our newsletter

Get latest tips, trends, and deals for free.

Email address

Subscribe

Use Cases of this MCP Server

• Movie Discovery and Exploration
  Developers can enable users to discover new movies by querying TMDB’s vast database, filtering by keywords, genres, or popularity.

• Personalized Recommendations
  AI assistants can fetch movie recommendations based on a user’s favorite films, improving user engagement in entertainment apps.

• Trend Monitoring
  Applications can display trending movies (daily or weekly), keeping users informed about popular content.

• Movie Detail Retrieval
  Bots or assistants can provide in-depth movie information, including cast, director, reviews, and more, enhancing user queries.

• Entertainment Chatbot Integration
  Integrate with chatbots to answer users’ questions about movies, actors, and upcoming releases in real time.

How to set it up

Windsurf

1. Ensure Node.js (v18+), npm (v8+), and TypeScript are installed.
2. Obtain a TMDB API key from TMDB .
3. Add the TMDB MCP Server to your configuration:

   {
     "mcpServers": {
       "tmdb": {
         "command": "npx",
         "args": ["@Laksh-star/mcp-server-tmdb@latest"]
       }
     }
   }
   

4. Save your configuration and restart Windsurf.
5. Set the TMDB API key via environment variable for security:

   {
     "env": {
       "TMDB_API_KEY": "your_api_key_here"
     }
   }
   

6. Verify setup by running a test query.

Claude

1. Confirm prerequisites (Node.js, npm, TypeScript) and obtain a TMDB API key.
2. Edit ~/Library/Application Support/Claude/config.json to include:

   {
     "mcpServers": {
       "tmdb": {
         "command": "npx",
         "args": ["@Laksh-star/mcp-server-tmdb@latest"]
       }
     }
   }
   

3. Save configuration and restart Claude Desktop.
4. Secure your API key with environment variables:

   {
     "env": {
       "TMDB_API_KEY": "your_api_key_here"
     }
   }
   

5. Validate integration by searching for a movie.

Cursor

1. Install Node.js, npm, and get a TMDB API key.
2. Open Cursor settings and locate MCP server configuration.
3. Add the following:

   {
     "mcpServers": {
       "tmdb": {
         "command": "npx",
         "args": ["@Laksh-star/mcp-server-tmdb@latest"]
       }
     }
   }
   

4. Use an environment variable for the API key:

   {
     "env": {
       "TMDB_API_KEY": "your_api_key_here"
     }
   }
   

5. Save and restart Cursor.

Cline

1. Setup Node.js, npm, and obtain the TMDB API key.
2. Find the MCP configuration file in Cline.
3. Insert:

   {
     "mcpServers": {
       "tmdb": {
         "command": "npx",
         "args": ["@Laksh-star/mcp-server-tmdb@latest"]
       }
     }
   }
   

4. Secure the API key:

   {
     "env": {
       "TMDB_API_KEY": "your_api_key_here"
     }
   }
   

5. Save, restart Cline, and test the server.

Note: Always secure your API keys using environment variables as shown above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "tmdb": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “tmdb” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion:
This MCP server provides strong movie data tooling and clear setup instructions, but lacks prompt templates and sampling support. It is well-suited for entertainment and movie assistant use-cases, though it could be more comprehensive with additional MCP features.

MCP Score

Rating:
Based on the available features, completeness, and documentation, this MCP server scores a 7/10. It is robust for movie-related tasks, but missing prompt and sampling support reduces its versatility for broader MCP-powered workflows.